Hardware In Loop Digital Twin Approach for Intelligent Optimization - Ai and Its Application to Complex Industrial Processes

An expert discussion of intelligent optimization control in complex industrial processes In A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration: AI and Its Application to Complex Industrial Processes, a team of distinguished researchers delivers an innovative new approach to integrating virtual mechanism data generated through coupled numerical simulation and orthogonal experimental design with real historical data. The book explains how to create a heterogenous ensemble prediction model for carbon monoxide emissions in municipal solid waste incineration (MSWI) processes. The authors focus on intelligent optimization control of MSWI processes based on hardware-in-loop DT platforms. They demonstrate AI-driven modeling, control, optimization algorithms in real-world applications, including virtual-real data hybrid-driven deep modeling and intelligent optimal controls based on multiple objectives. Additional topics include:

• A thorough introduction to numerical simulation modeling of whole industrial processes
• Comprehensive explorations of the design, implementation, and validation of hardware-in-loop digital twin platforms
• Practical discussions of AI-driven modeling, control, and optimization
• Fulsome descriptions of the skills required to address challenges posed by complex industrial processes

Perfect for environmental engineers and researchers, A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration will also benefit MSWI plant operators and managers, as well as AI and machine learning researchers and developers of environmental monitoring and control systems.

List of contents

List of Figures xvii
List of Tables xxix
About the Authors xxxiii
Preface xxxv
Abbreviations xxxvii
Symbol Meaning xliii
1 Introduction 1
1.1 Municipal Solid Waste Incineration (MSWI) Process and Optimal Control 1
1.1.1 Description of MSWI Process 1
1.1.2 Control Mode in Developed and Developing Countries 5
1.1.3 Difficulties in the Implementation of Optimal Control and Application 9
1.1.3.1 Description of Optimal Control for Complex Industrial Process 9
1.1.3.2 Requirements of the MSWI Process in Academic Research and Industrial Applications 13
1.1.3.3 Difficulties of AI Algorithm Research and Validation for MSWI Processes 14
1.1.4 Development of Optimal Control Research Based on Artificial Intelligence (AI) 16
1.2 AI-Based Modeling and Monitoring 17
1.2.1 Numerical Simulation Modeling 17
1.2.1.1 Brief Description of Numerical Simulation for MSWI Processes 17
1.2.1.2 Based on Commercial Software 19
1.2.1.3 Based on Self-developed Software 22
1.2.1.4 Difficulties of Numerical Simulation Modeling 22
1.2.1.5 Digital Twin (DT) Model Construction for MSWI 24
1.2.2 Combustion Process Modeling 26
1.2.2.1 Key Controlled Variables (CVs) Modeling 26
1.2.2.2 Auxiliary Variables (AVs) Modeling 27
1.2.3 Operational Indicators Modeling 28
1.2.3.1 Environmental Indicators (EIs) Modeling 28
1.2.3.2 Product Indicators (PIs) Modeling 29
1.2.3.3 Economic Indicators Modeling 30
1.2.4 Flame Status Monitoring 30
1.2.5 Operational Abnormal Monitoring 31
1.3 Control and Optimization Based on AI and DT 32
1.3.1 Control in On-site 32
1.3.1.1 Research of Automatic Combustion Control (ACC) System 32
1.3.1.2 Research of Non-ACC System 33
1.3.2 Control in Off-site 33
1.3.2.1 Single Input Single Output (SISO) Control 33
1.3.2.2 Multiple Input Multiple Output (MIMO) Control 34
1.3.3 Optimization of Pollution Emission 35
1.4 Hardware-in-Loop DT for MSWI Processes 36
1.4.1 Brief Description of Simulation Platform for Industrial Process 36
1.4.2 Simulation Platform in Terms of Real/Virtual Perspective 37
1.4.2.1 "Real-Real" Simulation Platform 37
1.4.2.2 "Real-Virtual" Simulation Platform 38
1.4.2.3 "Virtual-Real" Simulation Platform 39
1.4.2.4 "Virtual-Virtual" Simulation Platform 40
1.4.3 Difficulties of Simulation Platform for MSWI Process 41
1.5 Book's Structure 42
Part I 42
Part II 45
Part III 47
References 48
Part I Modeling and Monitoring Based on AI 67
2 Numerical Simulation and Modeling Analysis on Whole Industrial Process by Coupling Multiple Software 69
2.1 Simulated Plant and Simulation Modeling 69
2.1.1 Simulated MSWI Plant 69
2.1.1.1 Process Flow Description of the Simulated MSWI Plant 70
2.1.1.2 Slag, Fly Ash, and Leachate Treatment of the Simulated MSWI Plant 71
2.1.2 Simulation and Modeling Requirements 72
2.1.3 MSWI Process Description for Numerical Simulation 74
2.1.3.1 Mechanism Oriented Process Description 74
2.1.3.2 Mechanism Model Description 77
2.1.4 Numerical Simulation and Modeling Analysis Framework 91
2.2 Modeling Strategy with Virtual Data-driven 92
2.3 Modeling Implementation for Whole Process 94
2.3.1 Multi-Software-Coupled Whole-Process Numerical Simulation Module Under Benchmark Conditions 94
2.3.1.1 Solid-Phase Combustion Simulation on the Grate Based on FLIC 94
2.3.1.2 Gas-Phase Combustion in the Furnace Based on Fluent 96
2.3.1.3 Non-grate Solid-Phase Combustion Simulation in MSWI Process Based on Aspen Plus 97
2.3.2 Simulation Mechanism Data Acquisition Module Under Multiple Operating Conditions 100
2.3.3 Exhaust Emission Model Construction Module Based on Mimo-lrdt 101
2.3.4 Exhaust Emissions Analysis Module Based on Single/Dual Factors 103
2.4 Numerical Simulation and Modeling Results 103
2.4.1 Benchmark Condition Simulation Results 103
2.4.1.1 Data Description 103
2.4.1.2 Results of Solid MSW Combustion on the Grate 104
2.4.1.3 Results of Gas-Phase Combustion in the Furnace 106
2.4.1.4 Results of Non-grate Solid-Phase Combustion in MSWI Process 107
2.4.1.5 Comparison with the Actual Data 108
2.4.2 Results and Analysis of Multiple Operating Conditions 109
2.4.2.1 Typical Non-benchmark Condition Description 109
2.4.2.2 Typical Solid-Phase MSW Combustion Results Based on FLIC 109
2.4.2.3 Typical Gas-Phase Combustion Results Based on Fluent 110
2.4.2.4 Typical Non-grate Solid-Phase Combustion Results Based on Aspen Plus 116
2.4.2.5 Multiple Operating Conditions Results Based on Orthogonal Experimental Design 118
2.4.3 Construction Results of Exhaust Emission Model Based on Mimo-lrdt 118
2.4.4 Exhaust Emissions Cause and Effect Analysis Based on Single/Dual Factor 120
2.4.5 Discussion 124
2.5 Conclusion 124
References 125
3 Conventional Pollutant Deep Modeling Using Virtual Data and Real Data Hybrid-Driven 129
3.1 Virtual-Real Data-Driven Conventional Pollutant Modeling 129
3.1.1 Motivation 129
3.1.2 CO Description 130
3.1.3 CO Prediction Strategy 131
3.2 Real Data Hybrid-Driven Modeling Implementation 133
3.2.1 Offline Training Verification Phase 133
3.2.1.1 Multi-condition Virtual Mechanism Data Generation Module 133
3.2.1.2 Mechanism Mapping Model Module Based on LRDT 135
3.2.1.3 Real Data-Driven Model Module Based on LSTM 137
3.2.1.4 Heterogeneous Ensemble Module 140
3.2.2 Online Testing Verification Phase 142
3.2.2.1 Mechanism Mapping Model Module Based on LRDT 142
3.2.2.2 Real Data-Driven Model Module Based on LSTM 142
3.2.2.3 Heterogeneous Ensemble Module 142
3.3 Deep Modeling Results and Discussion 142
3.3.1 Data Description 142
3.3.2 Evaluation Indexes 143
3.3.3 Modeling Results and Discussion 143
3.3.3.1 Offline Training Verification Phase Results 143
3.3.3.2 Online Testing Verification Phase Results 152
3.3.4 Discussion on Model Hyperparameter 152
3.4 Conclusion 157
References 160
4 Trace Pollutant Modeling Using the Selective Ensemble Algorithm 163
4.1 Selective Ensemble Modeling Strategy 163
4.1.1 Motivation 163
4.1.2 DXN Generation Description of MSWI Process 164
4.1.3 DXN Soft Sensing Strategy 166
4.2 Trace Pollutant Modeling Implementation 168
4.2.1 Ensembled Submodel Building Module 168
4.2.1.1 BT Candidate Submodel Construction and Prediction Submodule 168
4.2.1.2 Candidate Submodel Bayesian Information Acquisition Submodule 170
4.2.1.3 Ensembled Submodel Selection Submodule 174
4.2.2 Ensembled Submodel Weighted Fusion Module 175
4.3 Data-Driven Ensemble Modeling Results and Discussion 176
4.3.1 Data Description 176
4.3.2 Evaluation Indicators 176
4.3.3 Benchmark Data Verification 180
4.3.3.1 Ensembled Submodel Construction Results 181
4.3.3.2 Weighted Prediction Results of Ensembled Submodel 183
4.3.3.3 Comparison of Experimental Results 184
4.3.4 Industrial Data Validation 186
4.3.4.1 Ensembled Submodel Construction Results 187
4.3.4.2 Weighted Prediction Results of the Ensembled Submodel 189
4.3.4.3 Comparison of Experimental Results 189
4.3.5 Hyperparameter Analysis for Different Datasets 196
4.4 Conclusion 201
References 201
5 Trace Pollutant Modeling Based on Semi-supervised Random Forest Optimization 205
5.1 Data-Driven Trace Pollutant Semi-supervised Random Forest Optimization Modeling Strategy 205
5.1.1 Semi-supervised Random Forest Optimization Modeling 205
5.1.2 Semi-supervised Regression Modeling and Optimization Research 206
5.1.3 Dioxin (DXN) Semi-supervised Soft Sensing Strategy 209
5.2 Data-Driven Trace Pollutant Modeling Implementation 212
5.2.1 Parameter Coding Design Module for Hybrid Optimization 212
5.2.2 Initialization and Decoding Module of the Hybrid Parameter 213
5.2.3 Fitness Evaluation Module for Multi-objective 215
5.2.3.1 Train the Random Forest (RF) Model Based on the Labeled Samples 215
5.2.3.2 Get the Pseudo-labeled Samples 217
5.2.3.3 Select the Pseudo-labeled Samples 217
5.2.3.4 Get the Mixed Sample Set 218
5.2.3.5 Train the RF Model Based on the Mixed Sample set 218
5.2.3.6 Evaluate the Fitness and Optimal Archive 218
5.2.4 Iterative Optimization and Optimal Solution Acquisition Module 218
5.2.4.1 Optimization Termination and Update 219
5.2.4.2 Optimal Solution Acquisition Module Based on the Pareto Solution Set 220
5.2.5 RF Model Construction Module Based on the Mixed Sample Set 220
5.3 Experimental Verification 221
5.3.1 Benchmark Dataset 221
5.3.1.1 Data Description 221
5.3.1.2 Experimental Results 221
5.3.1.3 Comparison with Other Methods 225
5.3.2 DXN Dataset 227
5.3.2.1 Data Description 227
5.3.2.2 Experimental Results 227
5.3.2.3 Comparison with Other Methods 231
5.3.3 Parameter Sensitivity Analysis 231
5.4 Conclusion 238
References 239
6 Combustion State Identification Using ViT-IDFC with Global Flame Feature 243
6.1 Combustion State Identification and Global Flame Feature 243
6.1.1 Motivation 243
6.1.2 Combustion State Recognition and Description 245
6.1.3 Combustion State Identification Strategy 248
6.2 State Monitoring Implementation Using ViT-IDFC 249
6.2.1 Building Module of Typical Combustion State Dataset 249
6.2.2 Extraction Module of ViT Depth Feature 250
6.2.2.1 Flame Image Feature Extraction Structure ViT 250
6.2.2.2 Feature Extraction and Selection Based on Transfer Learning 255
6.2.3 Construction Module of Improved Deep Forest Classification (IDFC) Model 256
6.3 Experimental Results 256
6.3.1 Data Collection 256
6.3.2 Typical Dataset Construction Results 258
6.3.3 Identification Model Construction Results 258
6.3.3.1 Evaluation Indicators and Environment Configuration 258
6.3.3.2 Identification Results of the Minist Dataset 261
6.3.3.3 Results of ViT Deep Feature Extraction 261
6.3.3.4 Results of IDFC Identification 262
6.3.4 Method Comparison Results 265
6.3.5 Discussion and Analysis 266
6.3.5.1 Experiment of Input Deep Feature Ablation 266
6.3.5.2 Sensitivity Analysis of the IDFC Model 272
6.4 Conclusion 273
References 273
7 Online Combustion Status Recognition of Using IDFC based on Convolutional Multi-Layer Feature Fusion 277
7.1 Convolutional Multi-layer Feature Fusion Based Online Combustion Identification 277
7.1.1 Motivation 277
7.1.2 Online Combustion State Identification Strategy 279
7.2 Convolutional-Feature-IDFC-Based Implementation 280
7.2.1 Data Collection and Analysis Phase 280
7.2.2 Offline Modeling Phase 281
7.2.2.1 Deep Feature Extraction Module Based on LeNet-5 281
7.2.2.2 Recognition Model Construction Module Based on CF 288
7.2.3 Online Recognition Phase 289
7.3 State Monitoring Results and Discussion 289
7.3.1 Data Collection and Analysis Phase Results 289
7.3.2 Offline Modeling Phase Results 290
7.3.2.1 Experimental Environment Configuration 290
7.3.2.2 Result of Method Comparison 290
7.3.2.3 Results of Offline Recognition 292
7.3.2.4 Sensitivity Analysis of Hyperparametric 292
7.3.3 Online Recognition Phase Results 292
7.3.4 Comprehensive Analysis 297
7.4 Conclusion 298
References 298
Part II Control and Optimization Based on AI and Digital Twin 301
8 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network (IT2FNN) for Furnace Temperature Control 303
8.1 Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network Control Strategy 303
8.1.1 Bayesian Optimization and Interval Type-2 Fuzzy Neural Network 303
8.1.2 Furnace Temperature Description 305
8.1.3 IT2FNN Control Strategy Based on Bayesian Optimization (bo-it2fnn) 307
8.2 BO-Based Interval Type-2 Fuzzy Neural Network Control 309
8.2.1 IT2FNN Controller Module 309
8.2.1.1 Antecedent Network of IT2FNN 309
8.2.1.2 Aftereffect Network of IT2FNN 311
8.2.2 Controller Parameter Learning Module 311
8.2.3 Controller Learning Rate Optimization Module 316
8.2.4 Stability Analysis 317
8.3 Simulation Results 320
8.3.1 Dataset Preparation and Evaluation Indicators 320
8.3.2 Control Results and Comparison 321
8.3.2.1 Controller Parameter Settings 321
8.3.2.2 Constant Setpoint Tracking Experimental Results 322
8.3.2.3 Variable Setpoint Tracking Experimental Result 330
8.3.3 Hyperparameter Analysis 338
8.4 Conclusion 339
References 340
9 Interval Type-2 Fuzzy Control with Multiple Event Triggers for Furnace Temperature Control 345
9.1 Type-2 Fuzzy Broad Control with Multiple Event Triggers 345
9.1.1 Motivation 345
9.1.2 Event-Triggering Mechanism 347
9.1.3 METM-IT2FBLS Control Strategy 349
9.2 METM-Based Interval Type-2 Fuzzy Broad Control 351
9.2.1 IT2FBLS Controller Module 351
9.2.2 Controller Parameter Learning Module 353
9.2.3 Control Law Trigger Update Module 354
9.2.4 Controller Structure Trigger Update Module 356
9.3 Stability Analysis 358
9.3.1 Control Law Triggering Update Process 358
9.3.1.1 Untracked Phase of the Control System 358
9.3.1.2 Tracked Phase of the Control System 359
9.3.2 Control Structure Triggering Update Process 360
9.3.2.1 Enhance Node Growth Case 360
9.3.2.2 Enhance Node Deletion Case 360
9.4 Simulation Results 362
9.4.1 Evaluation Indicators 362
9.4.2 Mathematical Simulation Experiment 362
9.4.2.1 Controlled Object and Hyperparameter Settings 362
9.4.2.2 Control Experimental Results 363
9.4.3 Industrial Process Data Experiment 363
9.4.3.1 Controlled Object and Hyperparameter Settings 363
9.4.3.2 Experiment Results of Constant Setpoint Value Control 367
9.4.3.3 Experimental Results of Variable Setpoint Control 370
9.4.4 Experimental Results of Hyperparameter Analysis 372
9.5 Conclusion 376
References 377
10 Intelligent Optimal Control of Furnace Temperature Using Multi-loop Controller and PSO Optimization 381
10.1 Multi-loop Controller Using PSO Optimization 381
10.1.1 Motivation 381
10.1.2 Intelligent Optimal Control Analysis 384
10.1.3 Furnace Temperature Optimization Strategy 390
10.2 Data-Driven Furnace Temperature Optimization 392
10.2.1 Controlled Object Model Module 392
10.2.2 Multi-loop Controller Module 395
10.2.3 Controlled Variable Setpoint Optimization Module 397
10.2.3.1 Pollution Emission Indicator Model Sub-module Based on CART 397
10.2.3.2 Setpoint Optimization Sub-module Based on PSO 398
10.3 Simulation Results 400
10.3.1 Data Description 400
10.3.2 Estimation Indices 400
10.3.3 Results and Comparisons 401
10.3.3.1 Controlled Object Model 401
10.3.3.2 Multi-loop Controller 405
10.3.3.3 Controlled Variable Setpoint Optimization 410
10.3.4 Discussion and Analysis 415
10.4 Conclusion 415
References 416
11 Data-Driven Multi-objective Intelligent Optimal Control of Industrial Process 419
11.1 Multiple Objectives Multiple Controlled Variables Optimization 419
11.1.1 Motivation 419
11.1.2 Multiple Controlled Variables and Pollution Emission Indicators Description 421
11.1.2.1 Key Controlled Variables and Manipulated Variable Description 421
11.1.2.2 Key Controlled Variables and Pollution Emission Indicators Description 424
11.1.3 Multi-objective Optimization Problem 425
11.1.4 Multiple Controlled Variables Setpoints Optimization Strategy 428
11.2 Data-Driven Multiple Controlled Variables Optimization Implementation 429
11.2.1 Whole-Process Model Construction Module 429
11.2.1.1 Serial Controlled Object Modeling Sub-module 429
11.2.1.2 Parallel Pollutant Indicator Modeling Sub-module 432
11.2.2 Multi-input and Multi-output Loop Controller Realization Module 432
11.2.3 Key Controlled Variable Optimal Setpoint Value Solution Module 434
11.2.3.1 Population Initialization Stage 434
11.2.3.2 Iterative Optimization Stage 434
11.2.3.3 Solution Acquisition Stage 437
11.3 Simulation Results 437
11.3.1 Description of Data Collection 437
11.3.2 Experiment Results on Historical Data 438
11.3.2.1 Results of Whole-Process Modeling Module 438
11.3.2.2 Results of Multi-input and Multi-output Loop Controller Module 438
11.3.2.3 Results of Multi-objective Optimization Module 450
11.3.2.4 Results of Proposed Intelligent Optimal Control Strategy 451
11.4 Conclusion 453
References 454
Part III Hardware-in-loop Digital Twin Platform Design and Validation 457
12 Description of Hardware-in-Loop Digital Twin Platform Requirements for Industrial Process 459
12.1 Overview 459
12.2 Laboratory Research on Platform Functionality Requirements 459
12.3 Industrial Applications on Platform Functionality Requirements 461
12.4 Platform Functional Requirements from a Flex Reconfiguration Perspective 463
12.5 Conclusion 466
13 Design and Realization of Hardware-in-Loop Digital Twin Platform 467
13.1 Digital Twin Functional Design 467
13.2 Hardware-in-Loop Structural Design 468
13.2.1 Overall Structure 468
13.2.2 Multimodal Historical Data-Driven System Structure 470
13.2.3 Security Isolation and Optimization Control System Structure 472
13.2.4 Multiple Input Multiple Output Loop Control System Structure 474
13.3 Hardware Setup 477
13.3.1 Network Connection 477
13.3.2 Hardware Selection 479
13.4 Software Design 479
13.4.1 Software System Composition 479
13.4.2 Multimodal Historical Data-Driven System Software Structure 482
13.4.3 Security Isolation and Optimization Control System Software Structure 483
13.4.4 Multiple Input Multiple Output Loop Control System Software Structure 484
13.4.5 Software Configuration 486
13.5 Platform Realization 487
13.5.1 Overall Implementation 487
13.5.2 Multimodal Historical Data-Driven System Implementation 487
13.5.3 Security Isolation and Optimization Control System Implementation 489
13.5.4 Multiple Input Multiple Output Loop Control System Implementation 491
14 Testing and Validation of Hardware-in-Loop Digital Twin Platform 495
14.1 System Effectiveness Testing and Verification 495
14.1.1 Multimodal Historical Data-Driven System Validation 495
14.1.2 Validation of Multiple Input Multiple Output Loop Control System 495
14.1.3 Safety Isolation and Optimization Control System Validation 497
14.2 Laboratory Scene Intelligent Algorithm Testing and Validation 500
14.2.1 AI-Based Modeling and Monitoring Algorithm Testing and Validation 500
14.2.1.1 Improved GAN-DFR for DXN Emission Concentration Soft Sensing 500
14.2.1.2 Combustion State Recognition Based on Vit-IDFC 501
14.2.1.3 Combustion Line Quantification Based on GAN and Siamese Network 503
14.2.2 AI-Based Control and Optimization Algorithm Testing and Validation 506
14.2.2.1 Furnace Temperature Control Based on Interval Type II FNN 506
14.2.2.2 Furnace Temperature Prediction Control Based on Self-organizing It2fnn 506
14.2.2.3 Multi-objective Intelligent Optimization Control for Multiple Controlled Variables 508
14.3 Intelligent Algorithm Transplantation Application in Industrial Scenarios 512
14.3.1 Multimodal Data Real-Time Acquisition System 512
14.3.1.1 Process Data Real-Time Acquisition System 512
14.3.1.2 Flame Video Real-Time Acquisition System 514
14.3.1.3 Industrial Testing and Application 515
14.3.2 DXN Emission Concentration Soft Sensing Based on Simulation Mechanism and LRDT 515
14.3.3 Future Prospects of Industrial Scene Transplantation 518
15 Summary and Outlook of Hardware-in-Loop Digital Twin Platform 519
15.1 Summary 519
15.2 Future AI Algorithm Research and Validation End-Edge-Cloud Platform 520
15.2.1 Overall Structure 521
15.2.2 Function Description 523
15.2.2.1 Multiple Modal Historical Data Synchronization Publishing System 523
15.2.2.2 Multimodal Data-Driven AI-Modeling System 524
15.2.2.3 End-Side Multiple Input Multiple Output Loop AI Control System 527
15.2.2.4 Edge-Side Safety Isolation AI Control System 528
15.2.2.5 Cloud-Side Safety Isolation AI Optimization System 529
15.2.2.6 AI Technology Evaluation System 530
15.2.3 Development Prospects 532
15.2.3.1 Multi-purpose AI Modeling Algorithm 532
15.2.3.2 Multi-location AI Control Algorithm 533
15.2.3.3 Multi-level AI Monitoring Algorithm 533
15.2.3.4 Multi-objective AI Optimization Algorithm 534
15.2.3.5 Multi-functional Verification Platform 534
Index 537

About the author

Jian Tang, PhD, is a Professor and Researcher with the Department of Artificial Intelligence and Automation in the Faculty of Information Technology at the Beijing University of Technology. Wen Yu, PhD, is a Professor and Head of Department of the Departamento de Control Automatico at CINVESTAV-IPN (National Polytechnic Institute) in Mexico City, Mexico. Junfei Qiao, PhD, is a Professor with the Beijing University of Technology and Director of Beijing Laboratory of Smart Environmental Protection in Beijing, China.

Product details

• Authors: Junfei Qiao, Jiangjun Tang, Jiangjun (Instituto Politecnico Nacional (Ci Tang, Wen Yu, Wen (Instituto Politecnico Nacional (Cinvestav Yu, Yu Wen
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Release: 03.11.2025, delayed
• EAN: 9781394354016
• ISBN: 978-1-394-35401-6
• No. of pages: 624
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Artificial Intelligence, TECHNOLOGY & ENGINEERING / Industrial Engineering, TECHNOLOGY & ENGINEERING / Industrial Technology, COMPUTERS / Artificial Intelligence / General, Electronics & Communications Engineering, Industrial chemistry and manufacturing technologies